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March 9 @ 7:00 am - 11:30 pm


As artificial intelligence (AI) evolves, concerns about its energy-intensive operations and environmental impact grow.

However, advancements such as Spiking Neural Networks (SNNs) and Lifelong Learning (L2) offer promising avenues to mitigate AI’s carbon footprint while leveraging its potential to combat climate change.

Spiking Neural Networks (SNNs) and Lifelong Learning (L2)

Spiking Neural Networks (SNNs):

  • Inspired by the human brain’s neural structure, SNNs operate based on discrete spikes or pulses of activity.
  • Unlike traditional artificial neural networks (ANNs), SNNs consume energy only when a spike occurs, making them highly energy efficient.
  • SNNs have shown to be up to 280 times more energy-efficient than ANNs, making them suitable for various applications, including space exploration and self-driving cars.

Lifelong Learning (L2):

  • Involves continuous learning, accumulating knowledge from previous tasks to aid future learning and problem-solving.
  • L2 mitigates the need for extensive retraining of AI models, reducing energy demands over time.
  • Algorithms enabling sequential training on multiple tasks with minimal forgetting facilitate continual learning.

Challenges Leading to High Carbon Footprint of AI

Growing Energy Consumption:

  • Proliferation of data centres driven by AI demand contributes significantly to global energy consumption.
  • By 2025, the IT industry, fuelled by AI, could consume up to 20% of global electricity and emit 5.5% of carbon emissions.

AI Training Emissions:

  • Training large AI models emits substantial carbon dioxide, with emissions comparable to several cars over their lifetimes.
  • AI systems like GPT-3 exhibit high emissions, highlighting the energy demands of large language models.

Hardware Consumption:

  • Specialised processors used in AI, such as GPUs, consume substantial power despite advancements in energy efficiency.

Cloud Computing Efficiency:

  • While cloud companies pledge carbon neutrality, AI deployment still poses environmental concerns due to energy consumption.

Water Footprint of AI:

  • Water-intensive cooling systems in data centres contribute to the water footprint of AI, with large models consuming significant amounts of freshwater.

Role of AI in Addressing Climate Change

Enhanced Climate Modelling:

  • AI can analyse vast climate data to improve models, aiding in predicting and adapting to climate-related disruptions.

Advancements in Material Science:

  • AI-driven research can develop sustainable materials, reducing energy consumption in various sectors like wind energy and transportation.

Efficient Energy Management:

  • AI optimises electricity usage from renewable sources and identifies energy-saving opportunities in smart grids and manufacturing.

Environmental Monitoring:

  • High-end AI systems detect and predict environmental changes in real-time, aiding in sustainable agriculture and conservation efforts.

Remote Data Collection:

  • AI-powered robots gather data in inaccessible environments, facilitating research and monitoring in areas like the Arctic and oceans.

Energy Efficiency in Data Centres:

  • AI-driven solutions optimise data centre operations, reducing energy consumption without compromising safety.

Making AI Sustainable

Transparency in Energy Usage:

  • Standardising measurements of AI’s carbon footprint enables accurate assessment and comparison of environmental impact.

Model Selection and Algorithmic Optimization:

  • Choosing smaller models and efficient algorithms conserves energy and computational resources.

Advancements in Quantum Computing:

  • Quantum computing offers superior computational capabilities, potentially revolutionising the efficiency of AI systems.

Renewable Energy Adoption:

  • Commitment to operating data centres with 100% renewable energy enhances AI’s sustainability.

Advancements in Hardware Design:

  • Specialised hardware tailored for AI applications, like Google’s TPUs, improves energy efficiency.

Innovative Cooling Technologies:

  • Exploration of energy-efficient cooling methods, such as liquid immersion cooling and underwater data centres, reduces environmental impact.

Government Support and Regulation:

  • Regulations mandating transparent reporting of AI’s carbon emissions and tax incentives for sustainable practices promote environmental responsibility.

Mains Question:

  1. Discuss the role of Spiking Neural Networks (SNNs) and Lifelong Learning (L2) in mitigating the carbon footprint of artificial intelligence (AI) while leveraging its potential to combat climate change. (150 WORDS)


March 9
7:00 am - 11:30 pm
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